You work for a retail company. You have been tasked with building a model to determine the probability of churn for each customer. You need the predictions to be interpretable so the results can be…

Churn prediction is a classification task (binary outcome: churn or not churn), so a regression model is unsuitable for directly determining the probability of churn.

While AutoML Tabular models can be configured to generate explanations, they often employ complex ensemble methods or neural networks that are inherently less interpretable than a Random Forest, and using a regression model for a classification task is incorrect.

Custom TensorFlow neural networks, especially deep ones, are generally considered 'black box' models and are inherently less interpretable than simpler models like Random Forests, making it harder to directly understand the reasons for individual predictions for targeted marketing.

Building a random forest classification model is appropriate for churn prediction, which is a binary outcome, and offers good interpretability. Random Forests can provide feature importance scores, helping understand which factors contribute most to churn probability, which is crucial for developing targeted marketing campaigns.